CN116080326A - Control method and system for semi-active suspension - Google Patents

Abstract

The application discloses a control method and a system of a semi-active suspension, comprising the following steps: determining an external characteristic curve of the shock absorber according to a pre-established variable damping shock absorber simulation model; acquiring random disturbance information of a road surface and performance parameters of a semi-active suspension; determining a fuzzy regulation strategy according to the performance parameters of the semi-active suspension and the random disturbance information of the road surface; according to the fuzzy regulation strategy, the regulated control parameters are determined, and the suspension dynamic deflection, the wheel dynamic load and the vehicle body acceleration are regulated according to the regulated control parameters, so that the peak values of the suspension dynamic deflection, the vehicle body acceleration and the vehicle body dynamic load can be effectively reduced.

Description

Control method and system of semi-active suspension

Technical Field

The present application belongs to the technical field of control systems, and in particular, relates to a control method and system for a semi-active suspension.

Background Art

As an important part of modern automobiles, suspension has a significant impact on ride comfort and driving safety. Traditional passive suspension systems do not require external energy input and have a simple structure, so they are widely used. However, they can only consume energy when the suspension is working, and their damping characteristics cannot change with road excitation. Active suspension can obtain a high-quality vibration isolation system and achieve the control goal of an ideal suspension, but it consumes a lot of energy, has high cost, and a complex structure. Semi-active suspension is passive control, with low actuator price, low energy consumption, simple structure, and its control quality is close to that of active suspension.

In recent years, control technologies based on fuzzy control and PID control have developed rapidly. Combining different control methods and taking advantage of their respective advantages can achieve better optimization results. For example, the combination of incremental algorithms and PID controllers improves the performance of semi-active suspension systems and improves the ride smoothness and operational stability of engineering vehicles. The particle swarm optimization algorithm is used to adjust the parameters of the PID sliding mode controller (SMC-PID), which improves the effect of the controller and the performance of the magnetorheological shock absorber.

At present, when studying the suspension system algorithm, the shock absorber is usually simplified into a spring model and its external characteristics are ignored. This leads to errors in the calculation and actual process of the semi-active suspension algorithm, and also limits the popularization of the suspension system algorithm.

Summary of the invention

The present application intends to provide a control method and system for a semi-active suspension to solve the deficiencies in the prior art. The technical problem to be solved by the present application is achieved through the following technical solutions.

In a first aspect, an embodiment of the present application provides a control method for a semi-active suspension, the method comprising:

According to the pre-established simulation model of the variable damping shock absorber, the external characteristic curve of the shock absorber is determined;

Obtaining random disturbance information of the road surface and performance parameters of the semi-active suspension;

Determining a fuzzy adjustment strategy according to the performance parameters of the semi-active suspension and the random disturbance information of the road surface;

According to the fuzzy adjustment strategy, the adjusted control parameters are determined, and according to the adjusted control parameters, the suspension dynamic deflection, the wheel dynamic load and the vehicle body acceleration are adjusted.

Optionally, the input of the pre-established variable damping shock absorber simulation model is different input currents and excitation speeds, and the output is the external characteristic curve.

Optionally, the road surface random disturbance information is an identifier of a road surface roughness coefficient.

Optionally, the pre-established fuzzy adjustment strategy includes determining a corresponding scaling factor of the variable universe through an input variable deviation and a rate of change of the input variable deviation.

Optionally, the input variable deviation and the input variable deviation change rate include five first fuzzy sets, and the first fuzzy sets respectively represent different fuzzy states, including PB for positive large, PM for positive medium, PS for positive small, ZE for zero, NS for negative small, NM for negative medium, and NB for negative large; the expansion factor of the variable domain includes five second fuzzy sets, and the second fuzzy sets represent the control trend of the opening and closing of the solenoid valve, and the second fuzzy sets include ZE for closed, S for small, M for medium, B for large, and K for open.

In a second aspect, an embodiment of the present application provides a control system for a semi-active suspension, the system comprising:

A first determination module is used to determine the external characteristic curve of the shock absorber according to a pre-established variable damping shock absorber simulation model;

An acquisition module, used to obtain road surface random disturbance information and performance parameters of the semi-active suspension;

A second determination module is used to determine a fuzzy adjustment strategy according to the performance parameters of the semi-active suspension and the road surface random disturbance information;

The adjustment module is used to determine the adjusted control parameters according to the fuzzy adjustment strategy, and adjust the suspension dynamic deflection, wheel dynamic load and vehicle body acceleration according to the adjusted control parameters.

Optionally, the input of the pre-established variable damping shock absorber simulation model is different input currents and excitation speeds, and the output is the external characteristic curve.

Optionally, the road surface random disturbance information is an identifier of a road surface roughness coefficient.

Optionally, the pre-established fuzzy adjustment strategy includes determining a corresponding scaling factor of the variable universe through an input variable deviation and a rate of change of the input variable deviation.

Optionally, the input variable deviation and the input variable deviation change rate include five first fuzzy sets, and the first fuzzy sets respectively represent different fuzzy states, including PB for positive large, PM for positive medium, PS for positive small, ZE for zero, NS for negative small, NM for negative medium, and NB for negative large; the expansion factor of the variable domain includes five second fuzzy sets, and the second fuzzy sets represent the control trend of the opening and closing of the solenoid valve, and the second fuzzy sets include ZE for closed, S for small, M for medium, B for large, and K for open.

The embodiments of the present application include the following advantages:

The control method and system of the semi-active suspension provided in the embodiment of the present application determine the external characteristic curve of the shock absorber according to a pre-established variable damping shock absorber simulation model; obtain road surface random disturbance information and performance parameters of the semi-active suspension; determine a fuzzy adjustment strategy according to the performance parameters of the semi-active suspension and the road surface random disturbance information; determine the adjusted control parameters according to the fuzzy adjustment strategy, and adjust the suspension dynamic deflection, wheel dynamic load, and body acceleration according to the adjusted control parameters, determine the external characteristics of the shock absorber through the established variable damping shock absorber simulation model, and combine the external characteristics of the shock absorber with the semi-active suspension, determine an adaptive variable domain fuzzy PID control strategy for the uncertainty of the semi-active suspension parameters and the random disturbance of the road surface, and determine the adjusted control parameters according to the adaptive variable domain fuzzy PID control strategy, and adjust the suspension dynamic deflection, wheel dynamic load, and body acceleration according to the adjusted control parameters, which can effectively reduce the peak values of the suspension dynamic deflection, body acceleration, and body dynamic load.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to more clearly illustrate the embodiments of the present application or the existing technical solutions, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

FIG1 is a flow chart of a control method of a semi-active suspension in one embodiment of the present application;

FIG2 is a schematic diagram of a control system of a semi-active suspension in one embodiment of the present application;

FIG3 is a schematic diagram of a 1/4 vehicle semi-active suspension system in one embodiment of the present application;

FIG4 is a random excitation signal of a Class B road surface in one embodiment of the present application;

FIG5 is a design flow chart of an adaptive fuzzy PID controller in one embodiment of the present application;

FIG6 is a membership function of E and EC in an embodiment of the present application;

FIG7 is a schematic diagram of a variable domain fuzzy control principle in one embodiment of the present application;

FIG. 8 is a structural block diagram of a control system embodiment of a semi-active suspension of the present application.

DETAILED DESCRIPTION

In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present application.

An embodiment of the present application provides a control method for a semi-active suspension, which is used to control the semi-active suspension. The embodiment is performed by a control system of the semi-active suspension.

1 , a flowchart of a control method for a semi-active suspension according to an embodiment of the present invention is shown. The method may specifically include the following steps:

S101, determining an external characteristic curve of the shock absorber according to a pre-established variable damping shock absorber simulation model;

Specifically, the input of the pre-established variable damping shock absorber simulation model is different input currents and excitation speeds, and the output is an external characteristic curve.

In the bench test, changes in input current and excitation speed can obtain different external characteristic curves of the shock absorber. The changes in the external characteristics of the shock absorber can guide the shock absorber. According to the CDC shock absorber data model obtained through the bench index test bench above, a corresponding variable damping shock absorber simulation model is established, and the structural parameters consistent with the bench test are set to obtain the external characteristic curve of the shock absorber through simulation.

S102, obtaining road surface random disturbance information and performance parameters of a semi-active suspension;

Among them, the road surface random disturbance information is an indicator of the road surface roughness coefficient.

Specifically, the performance parameters of the semi-active suspension include at least one or more of suspension dynamic deflection, wheel dynamic load, and vehicle body acceleration.

S103, determining a fuzzy adjustment strategy according to performance parameters of the semi-active suspension and random disturbance information of the road surface;

The pre-established fuzzy adjustment strategy includes determining the expansion factor of the corresponding variable domain through the input variable deviation and the input variable deviation change rate.

Specifically, the input variable deviation and the input variable deviation change rate include five first fuzzy sets, and the first fuzzy sets respectively represent different fuzzy states, including PB for positive large, PM for positive medium, PS for positive small, ZE for zero, NS for negative small, NM for negative medium, and NB for negative large; the expansion factor of the variable domain includes five second fuzzy sets, and the second fuzzy set represents the control trend of the opening and closing of the solenoid valve, and the second fuzzy set includes ZE for closed, S for small, M for medium, B for large, and K for open.

Specifically, the control system of the semi-active suspension determines different fuzzy adjustment strategies according to the performance parameters of the semi-active suspension and the random disturbance information of the road surface. That is to say, different fuzzy adjustment strategies are obtained for different performance parameters and random disturbance information of the road surface, namely, the adaptive variable universe fuzzy PID control strategy.

S104: Determine adjusted control parameters according to the fuzzy adjustment strategy, and adjust the suspension dynamic deflection, wheel dynamic load, and vehicle body acceleration according to the adjusted control parameters.

Specifically, the fuzzy adjustment strategy can recalculate the adjustment parameters of PID to obtain the adjusted control parameters. Through the adjusted control parameters, the performance parameters of the semi-active suspension are adjusted, that is, the suspension dynamic deflection, wheel dynamic load, and vehicle body acceleration are adjusted.

The control method of the semi-active suspension provided in the embodiment of the present application determines the external characteristic curve of the shock absorber according to a pre-established variable damping shock absorber simulation model; obtains road surface random disturbance information and performance parameters of the semi-active suspension; determines a fuzzy adjustment strategy according to the performance parameters of the semi-active suspension and the road surface random disturbance information; determines the adjusted control parameters according to the fuzzy adjustment strategy, and adjusts the suspension dynamic deflection, wheel dynamic load, and body acceleration according to the adjusted control parameters, determines the external characteristics of the shock absorber through the established variable damping shock absorber simulation model, and combines the external characteristics of the shock absorber with the semi-active suspension, determines an adaptive variable domain fuzzy PID control strategy for the uncertainty of the semi-active suspension parameters and the random disturbance of the road surface, determines the adjusted control parameters according to the adaptive variable domain fuzzy PID control strategy, and adjusts the suspension dynamic deflection, wheel dynamic load, and body acceleration according to the adjusted control parameters, which can effectively reduce the peak values of the suspension dynamic deflection, body acceleration, and body dynamic load.

A road surface adaptive semi-active suspension control system as shown in FIG2 , the semi-active suspension control system performs the following steps:

Step 1: Establish a shock absorber model, select the CDC shock absorber as the research object, set the vibration stroke to 40 mm, test the CDC shock absorber through the bench index test bench, input a sinusoidal excitation signal, and pick up the excitation.

In bench tests, changes in input current and excitation speed can produce different external characteristic curves of the shock absorber. Changes in the external characteristics of the shock absorber can guide the design of the shock absorber.

The hardware parameters obtained from the test are shown in Table 1:

Table 1

According to the CDC shock absorber data model obtained through the bench index test bench, a corresponding variable damping shock absorber simulation model is established, the structural parameters consistent with the bench test are set, and the shock absorber external characteristic curve is obtained by simulation.

The experimental comparison of the two is shown in Table 2:

Table 2

It can be seen from Table 2 that the force values and error ranges of the test results and simulation results are basically consistent, with the maximum error being 11.8% and the minimum error being 2.0%. The above analysis can illustrate the accuracy of the shock absorber model and shows that the model can be used for the study of semi-active suspension.

Step 2: Establish a simplified dynamic model of the 1/4 vehicle semi-active suspension. For the above semi-active suspension model, select the excitation graphs of different vehicle speeds on Class B road as the excitation signal of the semi-active suspension, establish a random road disturbance model, and input it into the semi-active suspension model of the coupled CDC shock absorber as the initial excitation signal source.

The simplified semi-active dynamics model of the quarter vehicle is shown in Figure 3. According to the physical model of the semi-active suspension and Newton's second law, the kinematic differential equations of the sprung mass and the unsprung mass in the vertical Z direction are obtained as follows:

Among them, _Mb is the sprung mass; _Mu is the unsprung mass; _Ks is the suspension spring stiffness; _Kw is the tire stiffness; _Cs is the suspension damping coefficient; _Zu is the unsprung mass displacement; _Zb is the sprung mass displacement; _Zw is the road input displacement; and U is the damping force of the semi-active suspension.

The state equation of the system is:

Where A satisfies:

B satisfies:

F satisfies:

F = [00-10] ^T .

为路面的输入。in:

Input for the road surface.

悬架动挠度(Z_b-Z_u)；轮胎动变形(Z_u-Z_w)作为输出变量。Select the body vibration acceleration

Suspension dynamic deflection (Z _b -Z _u ); tire dynamic deformation (Z _u -Z _w ) are used as output variables.

Then the output equation of the system is:

Y＝CX+DU

Where Y satisfies:

Where C satisfies:

D satisfies:

According to international standards, the time domain disturbance curves under different road conditions are different and are described in the form of road surface PSD. The following formula is usually used to fit the power spectrum of road surface excitation:

Where: the spatial frequency is n(m ^-1 ); the reference spatial frequency is n ⁰ ; usually n0=0.1(m ^-1 ). The road roughness coefficient is G _q (n ⁰ )(m ³ ), which is Gaussian white noise with a frequency index of 2.

The GB-7031-1986 standard divides the road roughness coefficient into 8 levels from A to H, among which Class A road surface is the corresponding expressway and its road condition, Class A road surface is the best road condition, Class E road surface is unpaved road surface, and Class H road surface is the worst road condition.

The eight-level classification standard for road roughness is shown in Table 3:

Table 3

For the above semi-active suspension model, a filtered white noise excitation signal of the random road disturbance model is established, and the excitation patterns at speeds of 30km/h, 70km/h, and 120km/h on a Class B road are selected as the excitation signals of the semi-active suspension.

The time domain signal of road surface excitation is as follows:

Where f ₀ is the lower cutoff frequency (Hz), q(t) is the road height (m), G _q (n ₀ ) is the road roughness coefficient (m ³ ), v is the vehicle speed (m/s), and w(t) is the white noise signal.

The random excitation signal of Class B road surface is shown in Figure 4.

Step 3, establish an adaptive variable domain fuzzy PID controller, the PID controller is directly connected to the suspension system, and the fuzzy controller is connected to the PID controller. The PID controller adjusts the PID parameters Kp, Ki and Kd in real time, so that the semi-active suspension adjusts the suspension dynamic deflection, wheel dynamic load and body acceleration respectively. The fuzzy controller can adjust the PID controller parameters in real time according to the dynamic performance of the car, and provides a two-input and three-output two-dimensional fuzzy controller. The input variables of the fuzzy controller are the deviation E and the deviation change rate EC, and the output variables are the correction values of the PID controller parameters Kp, Ki and Kd, as shown in Figure 5.

According to the actual control object, the vibration velocity deviation signal and the vibration acceleration deviation change rate of the vehicle body in the vertical direction are both in numerical form, so the input and output variables are fuzzy processed.

Five fuzzy sets are used to represent the fuzzy states of the input variables E and EC of the fuzzy controller. In the design, the fuzzy domains of the input variables E and EC and the output variables Kp, Ki and Kd are set to [-6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6]. For PID control, the fuzzy subsets of E and EC are [NB, NM, NS, ZO, PS, PM, PB].

According to the previous analysis of the suspension, the membership functions are all selected as triangular membership functions, the Mamdani type min-max method is selected as the fuzzy reasoning method, and the center of gravity method is selected as the fuzzy decision. The membership functions of E and EC are shown in Figure 6.

The membership function is expressed as:

Taking the deviation E and EC as the input of the controller, the corrected PID parameters are:

K _zp ＝K _p0 +q _kp K _p

K _zi = K _i0 + q _ki K _i

K _zd ＝K _d0 +q _kd K _d

Where: In the formula: _Kzp , _Kzi , _Kzd are the setting values of the final PID parameters; _Kp0 , _Ki0 , _Kd0 are the setting values of the initial PID parameters; _qkp , _qki and _qkd are the correction coefficients.

In order to avoid the phenomenon that the input and output variables are too large or too small relative to the universe, a variable universe fuzzy control method is proposed, and an adaptive variable universe fuzzy PID controller is established. The principle diagram of the adaptive variable universe fuzzy control is shown in Figure 7.

xi＝[-E, E](i＝1,2…,n) is the domain of input variables xi(i＝1,2,…,n), and Y＝[-U,U] is the domain of output variables y.

The fuzzy output response can be approximated as follows:

Can be changed to:

X _i (x _i )=[-α _i (x _i )E _i ,α _i (x _i )E _i ]

Y(y)＝[-β(y)U,β(y)U]

Wherein: variables α _i ( _xi ) (i＝1,2,...n), β(y) are the expansion factors of _Xi and Y respectively, α _i ( _xi )＝1-λexp(-kx ² ), λ∈(0,1), k>0.

Among them, the input variables of the established adaptive variable universe fuzzy PID controller are the deviation E and EC, and the output of the controller is the expansion factor of the variable universe. Through the controller, the three indicators of suspension dynamic deflection and body acceleration representing suspension comfort, and body dynamic load representing safety are optimized. It is ensured that the input and output theoretical control quantity domain expands or expands accordingly with the change of error, so that the fuzzy control has the ability to adapt to the change of the control object and constantly approaches the ideal control output in a more precise local area.

According to the actual control object, five fuzzy sets are used to represent the fuzzy states of the controller input deviations E and EC. The corresponding fuzzy subsets are PB for positive large, PM for positive medium, PS for positive small, ZE for zero, NS for negative small, NM for negative medium, and NB for negative large; and five fuzzy sets are used to represent the control trend of the solenoid valve opening and closing for the output expansion factor, namely, ZE for closed, S for small, M for medium, B for large, and K for open. In the design, the fuzzy domains of the input variables input and output are set to [-6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6].

According to the previous analysis of the suspension motion process, the fuzzy rule table can be obtained, as shown in Table 4 below:

Table 4

,The fuzzy rule table can be specifically described as follows: when the input variable E is NB, the other input variable EC is NB, NM, NS, ZE, PS, PM, PB, the corresponding output variables are PB, PB, PM, PS, PM, PB, PB; when the input variable E is NM, the other input variable EC is NB, NM, NS, ZE, PS, PM, PB, the corresponding output variables are PB, PM, PM, PS, PM, PM, PB; when the input variable E is NS, the other input variable EC is NB, NM, NS, ZE, PS, PM, PB, the corresponding output variables are PM, PM, PS, ZO, PS, PM, PM; when the input variable E is ZE, the other input variable EC is NB, NM, NS, ZE, PS, PM, PB, the corresponding output variables are PS, PS, ZO, ZO, ZO, PS, PS, PS respectively; when the input variable E is PS and the other input variable EC is NB, NM, NS, ZE, PS, PM, PB, the corresponding output variables are PM, PM, PS, ZO, PS, PM, PM respectively; when the input variable E is PM and the other input variable EC is NB, NM, NS, ZE, PS, PM, PB, the corresponding output variables are PB, PM, PM, PS, PM, PM, PB respectively; when the input variable E is PB and the other input variable EC is NB, NM, NS, ZE, PS, PM, PB, the corresponding output variables are PB, PB, PM, PS, PM, PB, PB respectively.

Step 4. In order to verify the effect of the designed controller, the entire suspension system is simulated and analyzed in the Matlab/Simulink environment. The established road model system is input into the semi-active suspension model as the initial excitation signal source. The variable domain adaptive fuzzy PID control model is input into the control module of the suspension system. The corresponding parameters are fixed and the variable domain fuzzy controller is used to self-tune the input and output fields of the control system. Adaptive fuzzy PID control is used to improve the dynamic and static characteristics of the system.

The control system monitors the corresponding output variables of the system, namely the performance parameters of the semi-active suspension, including evaluation indicators such as suspension dynamic deflection, vehicle body acceleration, and vehicle body dynamic load.

Based on the B-type road excitation, three different vehicle driving conditions are selected, namely low-speed driving (30km/h), medium-speed driving (70km/h), and high-speed driving (120km/h). The three evaluation indicators of vehicle body acceleration, suspension disturbance, and wheel dynamic load selected under PID control (PC), fuzzy control (FC), and variable universe fuzzy PID adaptive control (VAC) are simulated and compared. On this basis, the simulation results are further analyzed and processed, and the RMS value comparison is shown in Table 5.

Table 5

It can be seen from Table 5 that the three control strategies can reduce the amplitude of vehicle body acceleration, suspension dynamic deflection, and wheel dynamic load under Class B road excitation through the VAC controller. All cases show that the VAC controller can achieve good optimization results compared with other methods.

It can be seen that the body acceleration, suspension dynamic deflection and wheel dynamic load of the adaptive variable domain fuzzy PID semi-active suspension control system are greatly improved compared with the traditional single fuzzy PID control system and fuzzy control, and there are significant improvements in comfort, smoothness and handling stability.

In summary, an adaptive variable universe fuzzy PID semi-active suspension control system of an embodiment of the present invention obtains control variables and derives mathematical functions of the semi-active suspension by analyzing the dynamic performance of the semi-active suspension system coupled with the CDC shock absorber. In addition, a semi-active suspension simulation model based on the shock absorber bench test is established, so that the damping control of the vehicle semi-active suspension is more efficient and energy-saving. First, the external characteristics are measured using a bench test bench and a CDC shock absorber simulation model is established. Then, an excitation graph of different vehicle speeds on a certain road surface is selected as an excitation signal for the vehicle semi-active suspension dynamics model to obtain a random road disturbance model. An adaptive variable universe fuzzy PID controller is established, the PID controller is directly connected to the suspension system, and the fuzzy controller is connected to the PID controller. The fuzzy controller uses the deviation E and EC as the input of the controller, and uses a variable universe fuzzy controller to fuzzify the input and output variables. The established road model system is input as the initial excitation signal source into the semi-active suspension model coupled with the CDC shock absorber, and then the variable universe adaptive fuzzy PID control model is input into the control module of the suspension system, and the corresponding parameters are fixed, so as to quickly respond to different performance requirements of driving comfort and safety under different road disturbances. The present invention can effectively reduce the dynamic deflection of the rear suspension, the acceleration of the vehicle body and the peak value of the dynamic load of the vehicle body, thereby improving the driving smoothness of the vehicle.

The embodiment of the present invention tests the CDC shock absorber on a bench index test bench, establishes a corresponding variable damping shock absorber simulation model, sets structural parameters consistent with the bench test, and simulates the external characteristic curve of the shock absorber. A 1/4 vehicle semi-active suspension dynamics model is established, and an excitation graph of different vehicle speeds on a certain road surface is selected as the excitation signal of the semi-active suspension, and a random road disturbance model is established. A two-input and three-output two-dimensional fuzzy controller is provided, with deviations E and EC as the input of the controller, and the input and output variables are fuzzy processed.

The established road model system is used as the initial excitation signal source to input into the semi-active suspension model, and the variable universe adaptive fuzzy PID control model is input into the control module of the suspension system, and the corresponding parameters are fixed, and the variable universe fuzzy controller is used. The input and output fields of the control system are self-tuned to improve the control accuracy; the adaptive fuzzy PID control is used to improve the dynamic and static characteristics of the system.

Compared with the existing technology, the embodiments of the present application can achieve:

The external characteristics of the shock absorber are obtained through bench experiments and simulations, and combined with the semi-active suspension. Aiming at the uncertainty of suspension parameters and random disturbances of the road surface, an adaptive variable universe fuzzy PID control strategy is proposed, which can effectively reduce the peak value of the rear suspension dynamic deflection, body acceleration and body dynamic load.

By effectively combining road surface information and semi-active suspension status, an adaptive variable universe fuzzy PID controller is designed to quickly respond to different performance requirements of driving comfort and safety under different road disturbances, thereby improving the vibration reduction and stability performance of the system.

A direct relationship between the suspension system and the shock absorber has been established. In the bench test, changes in input current and excitation speed can obtain different external characteristic curves of the shock absorber, which can guide the design of the shock absorber. Compared with the selection of a single shock absorber, it has a more comprehensive control effect.

Another embodiment of the present application provides a control system for a semi-active suspension, which is used to execute the control method for the semi-active suspension provided in the above embodiment.

8 , a block diagram of a control system embodiment of a semi-active suspension of the present application is shown. The device may specifically include the following modules: a first determination module 801, an acquisition module 802, a second determination module 803 and an adjustment module 804, wherein:

The first determination module 801 is used to determine the external characteristic curve of the shock absorber according to a pre-established variable damping shock absorber simulation model;

The acquisition module 802 is used to acquire road surface random disturbance information and performance parameters of the semi-active suspension;

The second determination module 803 is used to determine the fuzzy adjustment strategy according to the performance parameters of the semi-active suspension and the random disturbance information of the road surface;

The adjustment module 804 is used to determine the adjusted control parameters according to the fuzzy adjustment strategy, and adjust the suspension dynamic deflection, wheel dynamic load, and vehicle body acceleration according to the adjusted control parameters.

The control system of the semi-active suspension provided in the embodiment of the present application determines the external characteristic curve of the shock absorber according to a pre-established variable damping shock absorber simulation model; obtains road surface random disturbance information and performance parameters of the semi-active suspension; determines a fuzzy adjustment strategy according to the performance parameters of the semi-active suspension and the random disturbance information of the road surface; determines the adjusted control parameters according to the fuzzy adjustment strategy, and adjusts the suspension dynamic deflection, wheel dynamic load, and body acceleration according to the adjusted control parameters, determines the external characteristics of the shock absorber through the established variable damping shock absorber simulation model, and combines the external characteristics of the shock absorber with the semi-active suspension, determines an adaptive variable domain fuzzy PID control strategy for the uncertainty of the semi-active suspension parameters and the random disturbance of the road surface, determines the adjusted control parameters according to the adaptive variable domain fuzzy PID control strategy, and adjusts the suspension dynamic deflection, wheel dynamic load, and body acceleration according to the adjusted control parameters, which can effectively reduce the peak values of the suspension dynamic deflection, body acceleration, and body dynamic load.

Another embodiment of the present application further supplements the control system of the semi-active suspension provided in the above embodiment.

Optionally, the input of the pre-established variable damping shock absorber simulation model is different input currents and excitation speeds, and the output is an external characteristic curve.

Optionally, the road surface random disturbance information is an identifier of a road surface roughness coefficient.

Optionally, the pre-established fuzzy adjustment strategy includes determining a corresponding scaling factor of the variable universe through an input variable deviation and a rate of change of the input variable deviation.

Optionally, the input variable deviation and the input variable deviation change rate include five first fuzzy sets, the first fuzzy sets respectively represent different fuzzy states, the first fuzzy sets include PB for positive large, PM for positive middle, PS for positive small, ZE for zero, NS for negative small, NM for negative middle, and NB for negative large; the expansion factor of the variable domain includes five second fuzzy sets, the second fuzzy sets represent the control trend of the opening and closing of the solenoid valve, the second fuzzy sets include ZE for closed, S for small, M for middle, B for large, and K for open.

As for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

The control system of the semi-active suspension provided in the embodiment of the present application determines the external characteristic curve of the shock absorber according to a pre-established variable damping shock absorber simulation model; obtains road surface random disturbance information and performance parameters of the semi-active suspension; determines a fuzzy adjustment strategy according to the performance parameters of the semi-active suspension and the random disturbance information of the road surface; determines the adjusted control parameters according to the fuzzy adjustment strategy, and adjusts the suspension dynamic deflection, wheel dynamic load, and body acceleration according to the adjusted control parameters, determines the external characteristics of the shock absorber through the established variable damping shock absorber simulation model, and combines the external characteristics of the shock absorber with the semi-active suspension, determines an adaptive variable domain fuzzy PID control strategy for the uncertainty of the semi-active suspension parameters and the random disturbance of the road surface, determines the adjusted control parameters according to the adaptive variable domain fuzzy PID control strategy, and adjusts the suspension dynamic deflection, wheel dynamic load, and body acceleration according to the adjusted control parameters, which can effectively reduce the peak values of the suspension dynamic deflection, body acceleration, and body dynamic load.

It should be noted that the above detailed description is exemplary and is intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the art to which the present application belongs.

It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should also be understood that when the terms "comprise" and/or "include" are used in this specification, it indicates the presence of features, steps, operations, devices, components and/or combinations thereof.

It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the terms used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein.

In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units that are not explicitly listed or inherent to these processes, methods, products, or apparatuses.

For ease of description, spatially relative terms, such as "above", "above", "on the upper surface of", "above", etc., may be used herein to describe the spatial positional relationship between a device or feature and other devices or features as shown in the figure. It should be understood that spatially relative terms are intended to include different orientations of the device in use or operation in addition to the orientation described in the figure. For example, if the device in the accompanying drawings is inverted, the device described as "above other devices or structures" or "above other devices or structures" will be positioned as "below other devices or structures" or "below other devices or structures". Thus, the exemplary term "above" may include both "above" and "below". The device may also be positioned in other different ways, such as rotated 90 degrees or in other orientations, and the spatially relative descriptions used herein are interpreted accordingly.

In the above detailed description, reference is made to the accompanying drawings, which form a part of this document. In the accompanying drawings, similar symbols typically identify similar components unless the context indicates otherwise. The illustrated embodiments described in the detailed description, drawings, and claims are not meant to be limiting. Other embodiments may be used, and other changes may be made, without departing from the spirit or scope of the subject matter presented herein.

The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims (10)

1. A method of controlling a semi-active suspension, the method comprising:

determining an external characteristic curve of the shock absorber according to a pre-established variable damping shock absorber simulation model;

acquiring random disturbance information of a road surface and performance parameters of a semi-active suspension;

determining a fuzzy regulation strategy according to the performance parameters of the semi-active suspension and the random disturbance information of the road surface;

and determining the adjusted control parameters according to the fuzzy adjustment strategy, and adjusting the suspension dynamic deflection, the wheel dynamic load and the vehicle body acceleration according to the adjusted control parameters.

2. The method according to claim 1, wherein the pre-established variable damping vibration damper simulation model is input with different input currents and excitation speeds, and is output with the outer characteristic curve.

3. The method for controlling a semi-active suspension according to claim 1, wherein the road surface random disturbance information is an identification of a road surface unevenness coefficient.

4. The method of claim 1, wherein the pre-established fuzzy tuning strategy includes determining the scaling factor of the corresponding variable domain by the input variable bias and the input variable bias rate of change.

5. The method of claim 4, wherein the input variable bias and the input variable bias change rate include five first fuzzy sets, the first fuzzy sets respectively representing different fuzzy states, the first fuzzy sets including PB positive, PM median, PS positive small, ZE zero, NS negative small, NM negative medium, NB negative large; the expansion factors of the variable domain comprise five second fuzzy sets, the second fuzzy sets represent the opening and closing control trend of the electromagnetic valve, and the second fuzzy sets comprise ZE being closed, S being small, M being medium, B being large and K being open.

6. A control system for a semi-active suspension, the system comprising:

the first determining module is used for determining an external characteristic curve of the shock absorber according to a pre-established variable damping shock absorber simulation model;

the acquisition module is used for acquiring random disturbance information of the road surface and performance parameters of the semi-active suspension;

the second determining module is used for determining a fuzzy regulation strategy according to the performance parameters of the semi-active suspension and the random disturbance information of the road surface;

the adjusting module is used for determining the adjusted control parameters according to the fuzzy adjusting strategy and adjusting the suspension deflection, the wheel dynamic load and the vehicle body acceleration according to the adjusted control parameters.

7. The control system of a semi-active suspension as recited in claim 6 wherein said pre-established variable damping shock absorber simulation model is input for different input currents and excitation speeds and output as said outer characteristic.

8. The control system of a semi-active suspension of claim 6, wherein the road surface random disturbance information is an identification of a road surface unevenness coefficient.

9. The control system of a semi-active suspension of claim 6, wherein the pre-established fuzzy tuning strategy includes determining a telescoping factor for a corresponding variable domain by an input variable bias and an input variable bias rate of change.

10. The control system of a semi-active suspension of claim 9, wherein the input variable bias and the input variable bias rate of change include five first fuzzy sets, the first fuzzy sets representing different fuzzy states, respectively, the first fuzzy sets including PB positive large, PM median, PS positive small, ZE zero, NS negative small, NM negative medium, NB negative large; the expansion factors of the variable domain comprise five second fuzzy sets, the second fuzzy sets represent the opening and closing control trend of the electromagnetic valve, and the second fuzzy sets comprise ZE being closed, S being small, M being medium, B being large and K being open.

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